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Strategic Project Lead – AI Post-Training (United States)

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Remote from
USA
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
30 Nov 2026
Listing views
55
Application actions
1
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AI Summary

The role, at a glance.

Appen is seeking a Strategic Project Lead to deliver AI post-training and model-evaluation data programs for AI clients. The role owns project scope, timelines, budgets, staffing, risk management, client communication, and escalation handling. It translates model-improvement goals into annotation workflows, quality rubrics, acceptance criteria, and measurable evaluation plans. Candidates need experience coordinating AI data operations or delivery work, with familiarity in model evaluation, preference data, annotation quality, or related GenAI workflows.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThe role combines end-to-end project ownership with specialized knowledge of AI post-training, data quality, and model-evaluation practices. Ambiguous client requirements, multiple workstreams, and quality-sensitive deliverables require sound judgment and proactive problem solving.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Highly competitive
$110,000
US market range (per year)$85k–$135k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The yearly cadence and USD market estimate are inferred for a full-time US-based AI data and project-delivery role; estimated market range is $85,000–$135,000 per year, with an estimated midpoint of $110,000.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you translate a client's model-improvement objective into an executable data program?

I would first clarify the target behavior, users, model constraints, success metrics, and acceptance criteria. I would then define the required data type, task workflow, annotation rubric, sampling plan, QA checks, pilot design, and reporting cadence, validating assumptions with the client before scaling.

Describe how you would investigate inconsistent quality results in an annotation or preference-data project.

I would segment the issue by annotator, task type, rubric category, source batch, and time period to identify patterns. I would review examples with subject-matter stakeholders, determine whether the cause is unclear guidance, training, tooling, or source-data quality, then update controls and measure whether the corrective action improves agreement and acceptance rates.

What metrics would you use to manage delivery quality for a model-evaluation program?

I would track completion against plan, acceptance rate, inter-annotator agreement where applicable, audit-defect rates, rework volume, turnaround time, escalation volume, and rubric adherence. The exact metrics would be tied to the client's evaluation objective and reported with clear thresholds and trends.

How do you handle an ambiguous or changing client requirement during an active project?

I document the ambiguity, explain the delivery and quality impact, and present concrete options with trade-offs. After gaining a decision from the appropriate stakeholder, I update the scope, workflow, documentation, staffing plan, and client communications so all teams operate from the same approved direction.

What is your approach to balancing timelines, budget, and data quality when they conflict?

I make the trade-offs visible early using risk and impact assessments rather than allowing hidden quality degradation. I prioritize critical quality controls, recommend a pilot or phased release when useful, and align the client on which variables can change while protecting the validity of the final dataset or evaluation outcome.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

About the Role

The Strategic Project Lead – AI Post-Training will help lead AI companies develop and improve frontier models through high-quality data programs. We’re looking for someone with experience in AI post-training, preference data, or model evaluation to lead projects that shape how models respond, reason, and perform.

You’ll own delivery while bringing practical knowledge of the data, evaluation methods, and quality standards behind model improvement. Working closely with clients and internal teams, you’ll help translate evolving AI research needs into clear workflows, reliable datasets, and measurable outcomes.

Your Impact

  • Own end-to-end project delivery, including scope, timelines, budgets, staffing, and risk management.
  • Lead client communication, status reporting, planning sessions, and escalations.
  • Translate model improvement objectives into data requirements, workflows, rubrics, and evaluation plans.
  • Define quality standards and acceptance criteria for preference data, model comparisons, and evaluation outputs.
  • Monitor delivery quality, investigate inconsistencies, and resolve ambiguous requirements with clients.
  • Advise on data design, evaluation approaches, and project feasibility based on your domain expertise.
  • Develop reusable guidelines, SOPs, and QA frameworks to improve consistency across programs.
  • Mentor Operations Specialists and contribute to project scoping, pilots, and new capability development.

What You Bring

  • 1–2+ years of experience in AI data operations, project coordination, or delivery, ideally in a GenAI or frontier AI environment.
  • Familiarity with at least one AI post-training or model evaluation workflow.
  • Experience coordinating projects or workstreams, managing stakeholders, and communicating with clients.
  • Understanding of data quality, annotation workflows, and quality assurance methods.
  • Ability to translate evolving requirements into clear workflows, rubrics, and measurable quality standards.
  • Strong troubleshooting and organizational skills, with the ability to manage multiple priorities.
  • Interest in current AI developments, including frontier models and post-training approaches.

Nice to Have

  • Experience with reinforcement learning from human feedback (RLHF), preference data, or model response evaluation.
  • Familiarity with rubric design, model comparison, benchmark development, or evaluation creation.
  • Experience assessing model responses for quality, accuracy, reasoning, or instruction following.
  • Exposure to AI companies, frontier AI labs, or model evaluation programs.
  • Bilingual capabilities, particularly German or a Nordic language.

Why You’ll Love Working Here

At Appen, we foster a culture of innovation, collaboration, and excellence. We value curiosity, accountability, and a commitment to delivering the highest quality AI solutions for frontier models.

You’ll work on complex challenges that shape the future of AI across industries and geographies, alongside talented people in a culture that values humility over ego. You’ll have the flexibility to deliver in a way that works for you and your team, supported by tools, resources, and development opportunities to continue to build your capability over time.

About Appen

Appen has been a leader in AI training data for over 30 years. We specialise in human generated data to train, fine tune, and evaluate models across generative AI, large language models, computer vision, and speech recognition. Our AI assisted data annotation platform and global crowd of more than 1 million contributors in over 200 countries support model pre training, supervised fine tuning, evaluation and benchmarking, safety and red teaming, and multilingual global expansion.

Compensation

Additional Information

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